Deep User Modeling by Behavior

Abstract

A system, method and non-transitory computer-readable medium are provided for deep user modeling of user behavior. According to the deep user modeling, user behavior vectors that represent historical user behaviors of a user are determined. Based on a concatenation of the user behavior vectors, a variable-length user behavior matrix is determined. The variable-length user behavior matrix is converted into a fixed-length embedding vector via a long short term memory network, and the fixed-length embedding vector is outputted to the user as a predicted target behavior.

Description

BACKGROUND AND SUMMARY OF THE INVENTION[0001]The present invention relates to a system, method, and non-transitory computer-readable medium for modeling user behavior based on user observable behavior sequence data.[0002]User profiling plays a central role in offering personalized service, deeper user understanding and modeling, and better service and user experience. We propose a unified algorithmic framework to deal with the user profile learning problem that aims to map the behavior objects to vectors of real numbers called “user embedding.” Such mapping is generated through in-depth machine learning to optimize the prediction task.[0003]User profile learning can be measured from the performance of downstream tasks. For downstream tasks like ranking in a recommendation system, a good learned user profile can significantly improve prediction accuracy when predicting future user actions, since it precisely characterizes the user group to enrich the personalized recommendation.[0004...

AI Technical Summary

Benefits of technology

[0002]User profiling plays a central role in offering personalized service, deeper user understanding and modeling, and better service and user experience. We propose a unified algorithmic framework to deal with the user profile learning problem that aims to map the behavior objects to vectors of real numbers called “user embedding.” Such mapping is generated through in-depth machine l

Problems solved by technology

It is still a challenge for both research and production.

This feature engineering procedure guided by human instinct may fail to fully represent the data itself, and it requires too much work.

However, such aggregation may lose information that could be precisely related with the object that needs to be predicted in the downstream application.

Another important issue is that the user behaviors are naturally context-aware, highly flexible, and sequential in time, and thus hard to model.

There might be a potential behavior drifting that leads to a change in a user's profile.

Also, it is difficult to have explicit supervisions like mapping or inferencing between any pair of different behaviors that could help build the new individual representations.

However, such mapping and modeling is very difficult to be optimally and quantitat

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